Environment-wide association study on childhood obesity in the U.S

Association between childhood obesity, trace elements, and heavy metals: Recent discoveries and future perspectives

Trace elements and heavy metals play pivotal roles in health status by regulating a myriad of vital biological functions. Abnormal metal homeostasis has been linked to a constellation of pathogenic complications, including oxidative stress, inflammatory processes, dyslipidemia, and impaired insulin-mediated metabolism of carbohydrates, thereby increasing the odds of developing childhood obesity and related comorbidities. Herein, we provide a comprehensive revision of recent literature on the association between childhood obesity, trace elements, and heavy metals. Further, we emphasize on the crucial importance of addressing the influence that interindividual variability factors (e.g., sex, age, genetic determinants, concomitance of comorbidities, and environmental factors) may have in modulating the susceptibility to disease development. Altogether, this review article represents a concise guide to better understand the involvement of metals in childhood obesity pathogenesis and discusses future needs with the aim of establishing robust biomarkers in the context of precision medicine.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study

AIM

To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM.

METHODS

A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables.

RESULTS

The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model.

CONCLUSIONS

Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.

Trace Elements in Portuguese Children: Urinary Levels and Exposure Predictors

Exposure to environmental chemicals during developmental stages can result in several adverse outcomes. In this study, the exposure of Portuguese children to Cu, Co, I, Mo, Mn, Ni, As, Sb, Cd, Pb, Sn and Tl was evaluated through the analysis of first morning urine through ICP-MS. Furthermore, we attempted to determine possible exposure predictors. The study sample consisted of 54% girls and 46% boys, with a median age of 10 years; 61% were overweight/obese and were put on a nutritionally oriented diet. For I, half of the population was probably in deficiency status. The median urinary concentrations (μg/L) were Cu 21.9, Mo 54.6, Co 0.76, Mn 2.1, Ni 4.74, As 37.9, Sb 0.09, Cd 0.29, Pb 0.94, Sn 0.45, Tl 0.39 and I 125.5. The region was a significant predictor for Cu, Co, Ni, As and Tl. Children living in an urban area had higher urinary levels, except for Co and Ni. Age was a significant predictor for Cu, I, Mo, Mn, Ni, Sb, Cd and Sn with urinary levels of these elements decreasing with age. No sex-related differences were observed. Diet and weight group were predictors for urinary Cu, Mn, Ni, Sb and As. Significant differences were observed between the diet/weight groups for Cu, Ni, Sb and As, with the healthy diet group presenting higher values.

The association between internal polycyclic aromatic hydrocarbons exposure and risk of Obesity-A systematic review with meta-analysis

Exposure to polycyclic aromatic hydrocarbons (PAHs) is emerging as a risk factor for obesity, but with conflicting findings. The aim of this systematic review is to investigate and summarize the current evidence towards the associations between PAHs exposure and risk of obesity. We conducted a systematic search of online databases, including PubMed, Embase, Cochrane Library, and Web of Science up to April 28, 2022. Eight cross-sectional studies with data from 68,454 participants were included. The present study illustrated that there was a significant positive association between naphthalene (NAP), phenanthrene (PHEN), and total OH-PAH metabolites and risk of obesity, the pooled OR (95% CI) was estimated at 1.43 (1.07, 1.90), 1.54 (1.18, 2.02), and 2.29 (1.32, 3.99), respectively. However, there was no significant association between fluorene (FLUO) and1-hydroxypyrene (1-OHP) metabolite and risk of obesity. Subgroup analyses showed that associations between PAHs exposure and risk of obesity were more apparent in children, female, smokers and developing regions.

Genetics: A Starting Point for the Prevention and the Treatment of Obesity

Obesity is a common, serious, and costly disease. More than 1 billion people worldwide are obese-650 million adults, 340 million adolescents, and 39 million children. The WHO estimates that, by 2025, approximately 167 million people-adults and children-will become less healthy because they are overweight or obese. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer. These are among the leading causes of preventable, premature death. The estimated annual medical cost of obesity in the United States was nearly $173 billion in 2019 dollars. Obesity is considered the result of a complex interaction between genes and the environment. Both genes and the environment change in different populations. In fact, the prevalence changes as the result of eating habits, lifestyle, and expression of genes coding for factors involved in the regulation of body weight, food intake, and satiety. Expression of these genes involves different epigenetic processes, such as DNA methylation, histone modification, or non-coding micro-RNA synthesis, as well as variations in the gene sequence, which results in functional alterations. Evolutionary and non-evolutionary (i.e., genetic drift, migration, and founder's effect) factors have shaped the genetic predisposition or protection from obesity in modern human populations. Understanding and knowing the pathogenesis of obesity will lead to prevention and treatment strategies not only for obesity, but also for other related diseases.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.

A review of practical statistical methods used in epidemiological studies to estimate the health effects of multi-pollutant mixture

Environmental risk factors have been implicated in adverse health effects. Previous epidemiological studies on environmental risk factors mainly analyzed the impact of single pollutant exposure on health, while in fact, humans are constantly exposed to a complex mixture consisted of multiple pollutants/chemicals. In recent years, environmental epidemiologists have sought to assess adverse health effects of exposure to multi-pollutant mixtures based on the diversity of real-world environmental pollutants. However, the statistical challenges are considerable, for instance, multicollinearity and interaction among components of the mixture complicate the statistical analysis. There is currently no consensus on appropriate statistical methods. Here we summarized the practical statistical methods used in environmental epidemiology to estimate health effects of exposure to multi-pollutant mixture, such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regressions, shrinkage methods (least absolute shrinkage and selection operator, elastic network model, adaptive elastic-net model, and principal component analysis), environment-wide association study (EWAS), etc. We sought to review these statistical methods and determine the application conditions, strengths, weaknesses, and result interpretability of each method, providing crucial insight and assistance for addressing epidemiological statistical issues regarding health effects from multi-pollutant mixture.

An exposome-wide association study on body mass index in adolescents using the National Health and Nutrition Examination Survey (NHANES) 2003-2004 and 2013-2014 data

Excess weight is a public health challenge affecting millions worldwide, including younger age groups. The human exposome concept presents a novel opportunity to comprehensively characterize all non-genetic disease determinants at susceptible time windows. This study aimed to describe the association between multiple lifestyle and clinical exposures and body mass index (BMI) in adolescents using the exposome framework. We conducted an exposome-wide association (ExWAS) study using U.S. National Health and Nutrition Examination Survey (NHANES) 2003–2004 wave for discovery of associations between study population characteristics and zBMI, and used the 2013–2014 wave to replicate analysis. We included non-diabetic and non-pregnant adolescents aged 12–18 years. We performed univariable and multivariable linear regression analysis adjusted for age, sex, race/ethnicity, household smoking, and income to poverty ratio, and corrected for false-discovery rate (FDR). A total of 1899 and 1224 participants were eligible from 2003–2004 and 2013–2014 survey waves. Weighted proportions of overweight were 18.4% and 18.5% whereas those for obese were 18.1% and 20.6% in 2003–2004 and 2013–2014, respectively. Retained exposure agents included 75 laboratory (clinical and biomarkers of environmental chemical exposures) and 64 lifestyle (63 dietary and 1 physical activity) variables. After FDR correction, univariable regression identified 27 and 12 predictors in discovery and replication datasets, respectively, while multivariable regression identified 22 and 9 predictors in discovery and replication datasets, respectively. Six were significant in both datasets: alanine aminotransferase, gamma glutamyl transferase, segmented neutrophils number, triglycerides; uric acid and white blood cell count. In this ExWAS study using NHANES data, we described associations between zBMI, nutritional, clinical and environmental factors in adolescents. Future studies are warranted to investigate the role of the identified predictors as early-stage biomarkers of increased BMI and associated pathologies among adolescents and to replicate findings to other populations.